RACE 618: Meta-analysis for genetic association studies

Transcription

1 RACE 618: Meta-analyss for genetc assocaton studes Prof. Ammarn Thakknstan, Ph.D. Tel: , e-mal:

2 Outlne of talk Introducton & background Data analyss Checkng HWE Poolng allele prevalence Checkng heterogenety of genetc effects Determnaton of gene effects One-stage approach: Regresson analyss Two-stage approach: Multvarate meta-analyss Determnng the best genetc model Publcaton bas

3 Background Wth the rse of genetc and molecular epdemology, populaton-based molecular assocaton studes have become a popular research desgn Atta J, Thakknstan A, D'Este C. Meta-analyses of molecular assocaton studes: methodologc lessons for genetc epdemology. Journal of clncal epdemology 2003;56(4): ground

4 Background Current paradgm Multple genes wll contrbute to a complex dsease Each gene wll have a small effect sze Typcal studes wll not have enough power to detect these small effects Therefore, there wll be an even greater need for meta-analyss

5 Meta-analyss n genetc assocaton studes Increasngly used for poolng data n tradtonal epdemology Increasng work on applyng ths to genetc lnkage studes BUT, lttle attenton has been focused on usng ths method n populatonbased genetc ssocaton studes

6 Methodologc ssues ) Adherence to tradtonal qualty for systematc revew & meta-analyss Locate studes Databases/search terms & strateges Selecton of studes Incluson/excluson Data extracton Two revewers Poolng data Fxed vs random-effect model Heterogenety test, Publcaton bas

7 ) Handlng of partcular genetc ssues Hardy-Wenberg Equlbrum (HWE) Two alleles: A/a wth frequences p, q Genotype frequences AA, Aa, aa: p 2, 2pq, q 2 HW dsequlbrum Alleles are not segregatng ndependently Non-random matng Genotypng error Populaton stratfcaton Selecton bas

8 Genetc models used n poolng Usual meta-analyses pool 2 groups Exp vs non-exp, Rx vs Placebo Gene: 2 alleles: A, a 3 genotypes: AA, Aa, aa Determnaton of genetc effects Per-allele approach Comparng allele frequences (A, a) Per-genotype approach Parwse comparson of genotypes AA vs aa, Aa vs aa, AA vs Aa Assumng a genetc model

9 Effect sze Domnant effect of A AA Aa aa

10 Effect sze Recessve effect of A AA Aa aa

11 Effect sze Codomnant/addtve effect of A AA Aa aa

12 Recommendatons Observe gudelnes for tradtonal epdemologcal meta-analyss Partcular attenton to heterogenety Check for HWE Need to decde how to pool data n a way that s senstve to genetc models wthout multple comparsons J Cln Epdemol Apr;56(4): ; Stat Med May 15;24(9):

13 Applyng basc meta-analyss One par each of the tme Multple comparsons Increase type I error Number of genotypes = 3 Number of comparsons 3C2 = 3 Total type I error = 1-{1-0.05} 3 = Regresson analyss Logstc regresson Lnear regresson

14 Data analyss 1. Checkng HWE For dchotomous outcomes, Case-controls, or cohorts, or cross-sectonal studes HWE should be assessed only n the control or non-dsease group for case-control, use data for the whole cohort/cross-sectonal study

15 For contnuous outcomes, Study desgns are usually cohort or crosssectonal studes, HWE should be checked consderng the subjects as one large group The ch-square goodness of ft s used to test devaton from HWE 2 d. f ( o e e k 11 ) 2

16 Table 2. Assessng HWE for CFH polymorphsm Genotype Observe frequency Expected frequency P value TT TC CC CFH, complement factor H

17 0.141 p - value 1 d.f , ) ( ) ( ) ( ) / ( ) 40 ( x xn p e x x x xn p p e x xn p e p p p x n n a aa a A Aa AA A a A allele A

18 genhw Genotype Observed Expected AA Aa aa total Allele Observed Frequency Std. Err A a total Estmated dsequlbrum coeffcent (D) = Hardy-Wenberg Equlbrum Test: Pearson ch2 (1) = Pr= lkelhood-rato ch2 (1) = Pr= Exact sgnfcance prob =

19 Handlng studes that do not comply wth HWE Senstvty analyses ncludng and excludng studes not n HWE. Include all studes, but those ~HWE wll be adjusted for the degree of dsequlbrum usng the nbreedng coeffcent (F) suggested by Trkalnos et al (Am J Epdemol, 163: 300, 2006)

20 2. Poolng prevalence A requrement of Human Genome Epdemology Network (HuGENet) Poolng prevalence of mnor allele n control group How often the gene s dentfed n general populaton Pool by ethncty

21 Poolng prevalence Thakknstan et al; Am J Epdemol 2005:16; 201 Anothasntawee et al; Cln Nephrol 2009;71: 244

22 var( p ) pq n

23 3. Checkng heterogenety of genetc effects Table 3. Data notatons for a dchotomous outcome Genotype Case Control OR AA n 11 n 10 OR 1 Aa n 21 n 20 OR 2 aa n 31 n 30 1 *

24 OR OR 1 2 n n n n n n n n

25 ^ k k ^ ^ p p k p n n n n ) OR ( ) OR ( w n n n n OR θ w OR w OR θ θ θ w Q ^ ^ ln var ln var 1 ) ln( ln ˆ ln ln ˆ ) ˆ ˆ (

26 Contnuous data Table 4. Data notaton for contnuous outcome Genotype n Mean SD AA n 1 Mean 1 SD 1 Aa n 2 Mean 2 SD 2 aa n 3 Mean 3 SD 3

27 k Q w (d D) ˆ 2 k w d ˆ 1 D k w 1 w 1 var( d )

28 SMD ( x x ) d 1 3 sd ( n 1) sd2 ( n 1) sd2 sd n n n d2 var( d ) n n (Cohen's 1 3 2( n 2) method) USMD d ( x x ) 1 3 sd2 2 var( d ) 1 sd 3 n n 1 3 If the Q test s sgnfcant OR I 2 25%, between -study varaton should be accounted n poolng

29 4. Determnaton of gene effects A) One-stage approach by regresson analyss Dchotomous outcome Logstc regresson Bnary regresson Posson regresson Contnuous outcome Lnear regresson

30 Fxed effect model for dchotomous outcome πj(x) ln (1 πj(x)) α 2 j1 β j (genotype j ) ε j

31 Random effect model π j (x) 2 ln α (1 π (x)) j 1 j β j ( genotype j ) j j Lkelhood rato test s appled by comparng - loglkelhoo(ll) of the null model wth the model contaned genotype varable.if effects s next explored. t s sgnfcant, the mode of gene

32 Fxed effect model Contnuous outcome 2 Y j α j1 β j ( genotype j ) j

33 Random effect model 2 Y j α j1 β j ( genotype j ) j j F test s used to assess gene effects by comparng the full model wth the model wthout genotype varable.

34 B) Two-stage approach usng multvarate (mv) meta-analyss The relatve gene effects (e.g., d 1 and d 2 for contnuous outcome; OR 1 and OR 2 for dchotomous outcome) are smultaneously pooled at once Model Y ~ N(, V ~ N( X ), )

35 Y s a vector of genotype effects estmated from each study, V s a varance-covarance matrx of genotype effects (.e., OR 1 & OR 2 ), µ s study-specfc mean vector, X s study-genotype matrx. The mvmeta ams to pool gene effects,.e., β coeffcents and between-study varancecovarance matrx. Degree of heterogenety I 2 s also estmated for each comparson.

36 C) Two-stage approach usng IPD meta-analyss Perform two-stage nverse-varance ndvdual patent data meta-analyss The same concept as mvmeta,.e., estmate coeffcent & SE, then pool coeffcents across studes Type of model use depends on type of outcome, e.g., logt, regress

37 5. Determnng the best genetc model OR Mean dfference Mode of effects OR OR 1and OR OR OR 1and OR D D 0 and D D D 0 and D Recessve effect domnant effect codomnant (addtve) effect OR OR 1and OR OR 1 D D 0 and D D Or or OR OR 1 2 1and OR OR D 1 D 2 0 and D 1 D 3 0

38 Table 5. Multple comparsons of genotype effects and possble modes of nhertance *D s pooled mean dfference, OR s pooled odds rato Mode of nhertance AA versus aa Aa versus aa (D 1 or OR 1 ) * (D 2 or OR 2 ) AA versus Aa (D 3 or OR 3 ) Recessve Domnant Complete overdomnant Codomnant

39 By Mnell C et al (Int J Epdemol, 34: 1319, 2005): Bayesan approach ln ln OR OR Aa AA λ Gene effects 0 Recessve effect 1 Domnant effect 0.5 Addtve effect

40 Example 5. Assess assocaton between CFH Y402H polymorphsm & AMD The Y402H polymorphsm (OMIM# ) s defned by a substtuton of tyrosne to hstdne at codon 402 of the CFH gene A regon of the proten that bnds heparn and C- reactve proten Has an ant-nflammatory role TC, 3 genotypes TT, TC, CC

41 Genotype frequences between case and control groups Authors AMD Control N Genotype N Genotype TT TC CC TT TC CC Conley YP Edwards AO Hageman GS Klen RJ Magnusson KP Rvera A* Soued EH Zarepars S

42 Step of analyss Checkng HWE n control groups Pool mnor allele prevalence Checkng heterogenety of ORs Assess overall gene effects Assess mode of genetc effects Am J Epdemol Jun 15;173(12): ; Stat Med May 15;24(9):

43 Table7. Allelc data and estmaton of the pooled prevalence of the CFH Y402H allele Authors HWE N T C P-value Frequency Allele Frequency Allele Var(p) Conley YP Edwards AO Hageman GS Klen RJ Magnusson KP Rvera A* Soued EH Zarepars S Pooled prevalence *Not ncluded n the man poolng

44 Heterogenety OR1 Ch-square = (d.f. = 6) p = I-square = 69.0% OR2 Ch-square = (d.f. = 6) p = I-square = 58.7%

45 Gene effects Apply a mxed-effect logstc regresson π j (x) 2 ln α (1 π (x)) j 1 j β j ( genotype j ) j j

46 Modfy APD to ndvdual patent data (IPD) Step 1: reshape genotype from wde-wde to wde-long ren (TT_case TC_case CC_case) (gene11 gene12 gene13) ren (TT_cont TC_cont CC_cont) (gene01 gene02 gene03) reshape long gene0 gene1, (Study) j(genotype) Step 2: reshape group from wde-long to long-long reshape long gene, (Study genotype) j(gr) tab genotype gr [ freq=gene] Expand summary data to IPD expand gene bysort Study: tab genotype gr May need to drop record were cell = 0

47 The mxed-effect logt model wth panel data approach xtlogt gr.genotype f Study ~=8, (Study) nolog lncom 3.genotype-2.genotype pwcompare genotype, or est store A Test overall gene effect xtlogt gr f Study ~=8, (Study) lrtest A

48 Mult-level regresson Use melogt, meqrlogt, or xtmelogt command melogt gr.genotype f Study ~=8 Study: melogt, or pwcompare genotype, eform Unadjusted exp(b) Std. Err. [95% Conf. Interval] gr genotype 2 vs vs vs

49 Forrest plot tab genotype, gen(genotype) set more off xtmelogt gr genotype3 genotype2 f Study ~=8 /// Study:genotype3, nolog or pdforest genotype3, label(author Year) or qu xtmelogt gr genotype2 genotype3 f Study ~=8 Study: /// genotype2, nolog pdforest genotype2, label(author Year) or

50 Two-stage usng IPD meta DerSmonan-Lad method pdmetan, study(author) random or: /// logt gr genotype3 genotype2 f Study~=8 pdmetan, study(author) random or: /// logt gr genotype2 genotype3 f Study~=8

51 OR3 (CC vs TT) OR2 (TC vs TT) Author Odds Rato (95% CI) % Weght Author Odds Rato (95% CI) % Weght Conley YP Edwards AO et al (5.32, 24.39) 4.54 (2.70, 7.65)??10.99??15.40 Conley YP Edwards AO et al 4.97 (2.66, 9.28) 2.14 (1.43, 3.18)????9.10??15.14 Hageman GS et al 5.44 (3.82, 7.77)??18.98 Hageman GS et al 2.53 (1.93, 3.31)??20.24 Klen at al 8.50 (2.80, 25.79)????6.84 Klen at al 1.89 (0.80, 4.51)????5.58 Magnusson et al Soued et al Zarepars S et al Overall (I-squared = 69.0%, p = 0.004) 4.13 (3.28, 5.20) 6.84 (3.07, 15.21) (7.05, 19.14) 6.44 (4.57, 9.08)??21.51??10.42?? Magnusson et al Soued et al Zarepars S et al Overall (I-squared = 58.7%, p = 0.024) 1.87 (1.55, 2.25) 2.99 (1.61, 5.57) 3.03 (2.15, 4.28) 2.52 (2.00, 3.16)??23.67????9.13?? NOTE: Weghts are from Random-effects; DerSmonan-Lard estmator NOTE: Weghts are from Random-effects; DerSmonan-Lard estmator

52 Two-stage usng mvmeta vs networkmeta Requre b V b = lnorj V = varance-covarance matrx These two parameter-matrxes can be prepared usng mvmeta_make command, agan requre IPD

53 Requred data: long format for genotype & d n Study d n genotype ren (TT_case TC_case CC_case) (gene11 gene12 gene13) ren (TT_cont TC_cont CC_cont) (gene01 gene02 gene03) reshape long gene0 gene1, (Study) j(genotype) lst Study genotype gene1 gene0 n 1/ Study genotype gene1 gene

54 gen ngenotype =gene1+gene0 ren gene1 event1 lst Study genotype event1 gene0 ngenotype n 1/ Study genotype event1 gene0 ngenot~e Setup data as network data network setup event1 ngenotype, or studyvar(study) trtvar(genotype) armvars(drop)

55 Droppng arm-level varables: Treatments used A (reference): 1 B: 2 C: 3 Measure Studes ID varable: Number used: 8 IDs wth zero cells: Network nformaton Components: D.f. for nconsstency: 0 D.f. for heterogenety: 14 Current data Data format: Desgn varable: Estmate varables: Varance varables: Command to lst the data: gene0 Log odds rato Study [none] 1 (connected) augmented _desgn _y* _S* lst Study _y* _S*, noo sepby(_desgn)

56 . lst Study _desgn _y_b _y_c _S_B_B _S_B_C _S_C_C Study _desgn _y_b _y_c _S_B_B _S_B_C _S_C_C A B C A B C A B C A B C A B C A B C A B C A B C

57 network meta consstency f Study~=8, eform Command s: mvmeta _y _S f Study~=8, eform bscovarance(exch 0.5) longparm suppress(uv mm) vars(_y_b _y_c) Note: usng method reml Note: usng varables _y_b _y_c Note: 7 observatons on 2 varables Note: varance-covarance matrx s proportonal to.5*i(2)+.5*j(2,2,1) ntal: log lkelhood = Iteraton 4: log lkelhood = Multvarate meta-analyss Varance-covarance matrx = proportonal.5*i(2)+.5*j(2,2,1) Method = reml Number of dmensons = 2 Restrcted log lkelhood = Number of observatons = exp(coef) Std. Err. z P> z [95% Conf. Interval] _y_b _cons _y_c _cons

58 Forest plot network forest f Study~=8, eform xlne(1) xlne( ) xlab( ) Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 Study 7 All studes Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 Study 7 All studes Study 1 Study 2 Study 3 Study 4 Study 5 Study 6 Study 7 All studes 2 vs. 1 3 vs. 1 3 vs Odds rato Studes Pooled overall Graphs by column

59 Contnuous outcome: BsmI & BMD BsmI BB = 1 Bb = 2 Bb = 3 Outcome: mean BMD Two-stage approach usng mvmetaanalyss va network meta command Requre long data for n mean sd

60 lst study n1 mean1 sd1 n2 mean2 sd2 n3 mean3 sd3 n 1/3 study n1 mean1 sd1 n2 mean2 sd2 n3 mean3 sd reshape long n mean sd, (study) j(genotype) lst study n mean sd genotype n 1/ study n mean sd genotype

61 . network setup mean sd n, studyvar(study) trtvar(genotype) Treatments used A: (reference): 1 B: 2 C 3 Measure Standard devaton poolng: Studes ID varable: Mean dfference off Number used: 13 Network nformaton Components: D.f. for nconsstency: 0 D.f. for heterogenety: 24 Current data Data format: Desgn varable: Estmate varables: Varance varables: Command to lst the data: study 1 (connected) augmented _desgn _y* _S* lst study _y* _S*, noo sepby(_desgn)

62 lst study _desgn _y* _S_B_B _S_C_C _S_B_C study _desgn _y_b _y_c _S_B_B _S_C_C _S_B_C A B C A B C A B C

63 network meta c Multvarate meta-analyss Varance-covarance matrx = proportonal.5*i(2)+.5*j(2,2,1) Method = reml Number of dmensons = 2 Restrcted log lkelhood = Number of observatons = Coef. Std. Err. z P> z [95% Conf. Interval] _y_b _cons _y_c _cons lncom _b[_y_c: _cons] - _b[_y_b: _cons] ( 1) - [_y_b]_cons + [_y_c]_cons = Coef. Std. Err. z P> z [95% Conf. Interval] (1)

64 network forest Study 1 Study 10 Study 16 Study 20 Study 30 Study 37 Study 43 Study 48 Study 57 Study 59 Study 63 Study 68 Study 70 All studes Study 1 Study 10 Study 16 Study 20 Study 30 Study 37 Study 43 Study 48 Study 57 Study 59 Study 63 Study 68 Study 70 All studes 2 vs. 1 3 vs. 2 Study 1 Study 10 Study 16 Study 20 Study 30 Study 37 Study 43 Study 48 Study 57 Study 59 Study 63 Study 68 Study 70 All studes 3 vs Mean dfference Studes Pooled overall Graphs by column

65 BB 0.02 (-0.00,0.04) 0.03 (0.01,0.05) (-0.04,0.00) Bb 0.01 (-0.01,0.03) (-0.05,-0.01) (-0.03,0.01) bb Multple comparsons: netleague, label(bb Bb bb) sort(bb Bb bb ) export("c:\data\bsm.xlsx") Above dagonal cell, parwse comparson between genotype lne on column vs genotype lnes on the left row Below dagonal, parwse comparson between genotype n column vs genotype n the rgh row